





'''simple docstring'''






import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)






def   __A    (				a_    :	nn.ModuleList   ,a_    :	nn.ModuleList   ,a_    :	List[int]						):
		lowerCAmelCase  :     List[str]       						= nn.ModuleList([src_layers[i] for i in layers_to_copy]						)
		assert len(a_						) == len(a_						), f'''{len(a_						)} != {len(a_						)}'''
		dest_layers.load_state_dict(layers_to_copy.state_dict()						)


lowerCAmelCase									=   {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
lowerCAmelCase									=   {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}






def   __A    (				a_    :	Dict   ,a_    :	Dict						):
		try:
				lowerCAmelCase  :     Optional[int]       						= LAYERS_TO_COPY[n_teacher][n_student]
				return val
		except KeyError:
				if n_student != n_teacher:
						warnings.warn(
						    f'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
						    f''' {n_student}'''						)
				return list(range(a_						)						)






def   __A    (				a_    :	Dict   ,a_    :	str						):
		if n_student > n_teacher:
				raise ValueError(f'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'''						)
		elif n_teacher == n_student:
				return list(range(a_						)						)
		elif n_student == 1:
				return [n_teacher - 1]
		else:
				return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def   __A    (				a_    :	Union[str, PreTrainedModel]   ,a_    :	Union[str, Path] = "student"   ,a_    :	Union[int, None] = None   ,a_    :	Union[int, None] = None   ,a_    :	Optional[Any]=False   ,a_    :	Optional[Any]=None   ,a_    :	List[str]=None   ,**a_    :	Optional[Any]   ,):
		lowerCAmelCase  :     Optional[int]       						= "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
		assert (e is not None) or (d is not None), _msg
		if isinstance(a_   ,a_						):
				AutoTokenizer.from_pretrained(a_						).save_pretrained(a_						)  # purely for convenience
				lowerCAmelCase  :     Tuple       						= AutoModelForSeqaSeqLM.from_pretrained(a_						).eval()
		else:
				assert isinstance(a_   ,a_						), f'''teacher must be a model or string got type {type(a_						)}'''
		lowerCAmelCase  :     str       						= teacher.config.to_diff_dict()

		try:
				lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= teacher.config.encoder_layers, teacher.config.decoder_layers
				if e is None:
						lowerCAmelCase  :     List[str]       						= teacher_e
				if d is None:
						lowerCAmelCase  :     List[str]       						= teacher_d
				init_kwargs.update({"encoder_layers": e, "decoder_layers": d}						)
		except AttributeError:  # T5
				if hasattr(teacher.config   ,"num_encoder_layers"						):
						lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
				else:
						lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= teacher.config.num_layers, teacher.config.num_decoder_layers
				if e is None:
						lowerCAmelCase  :     str       						= teacher_e
				if d is None:
						lowerCAmelCase  :     Tuple       						= teacher_d
				if hasattr(teacher.config   ,"num_encoder_layers"						):
						init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d}						)
				else:
						init_kwargs.update({"num_layers": e, "num_decoder_layers": d}						)

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
		init_kwargs.update(a_						)

		# Copy weights
		lowerCAmelCase  :     Optional[Any]       						= teacher.config_class(**a_						)
		lowerCAmelCase  :     int       						= AutoModelForSeqaSeqLM.from_config(a_						)
		# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
		lowerCAmelCase  :     Union[str, Any]       						= student.load_state_dict(teacher.state_dict()   ,strict=a_						)
		assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

		if copy_first_teacher_layers:  # Our copying is done. We just log and save
				lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= list(range(a_						)						), list(range(a_						)						)
				logger.info(
				    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
				    f''' {save_path}'''						)
				student.save_pretrained(a_						)
				return student, e_layers_to_copy, d_layers_to_copy

		# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
		if e_layers_to_copy is None:
				lowerCAmelCase  :     List[int]       						= pick_layers_to_copy(a_   ,a_						)
		if d_layers_to_copy is None:
				lowerCAmelCase  :     List[int]       						= pick_layers_to_copy(a_   ,a_						)

		try:
				if hasattr(
				    a_   ,"prophetnet"						):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
						copy_layers(teacher.prophetnet.encoder.layers   ,student.prophetnet.encoder.layers   ,a_						)
						copy_layers(teacher.prophetnet.decoder.layers   ,student.prophetnet.decoder.layers   ,a_						)
				else:
						copy_layers(teacher.model.encoder.layers   ,student.model.encoder.layers   ,a_						)
						copy_layers(teacher.model.decoder.layers   ,student.model.decoder.layers   ,a_						)
		except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
				copy_layers(teacher.encoder.block   ,student.encoder.block   ,a_						)
				copy_layers(teacher.decoder.block   ,student.decoder.block   ,a_						)
		logger.info(
		    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''						)
		lowerCAmelCase  :     Any       						= {
		    "teacher_type": teacher.config.model_type,
		    "copied_encoder_layers": e_layers_to_copy,
		    "copied_decoder_layers": d_layers_to_copy,
		}
		student.save_pretrained(a_						)
		# Save information about copying for easier reproducibility

		return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
					fire.Fire(create_student_by_copying_alternating_layers)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   list[tuple[int, int]]

lowerCAmelCase									=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase									=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     int       						= pos_x
			lowerCAmelCase  :     Union[str, Any]       						= pos_y
			lowerCAmelCase  :     Optional[Any]       						= (pos_y, pos_x)
			lowerCAmelCase  :     Union[str, Any]       						= goal_x
			lowerCAmelCase  :     Tuple       						= goal_y
			lowerCAmelCase  :     str       						= g_cost
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     List[Any]       						= self.calculate_heuristic()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= abs(self.pos_x - self.goal_x						)
			lowerCAmelCase  :     Any       						= abs(self.pos_y - self.goal_y						)
			return dx + dy





	def __lt__( self		,   a_						):
			return self.f_cost < other.f_cost






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= Node(start[1]		,   start[0]		,   goal[1]		,   goal[0]		,   0		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= Node(goal[1]		,   goal[0]		,   goal[1]		,   goal[0]		,   99_999		,   a_						)

			lowerCAmelCase  :     Tuple       						= [self.start]
			lowerCAmelCase  :     list[Node]       						= []

			lowerCAmelCase  :     Tuple       						= False



	def 	_lowerCamelCase     ( self						):
			while self.open_nodes:
					# Open Nodes are sorted using __lt__
					self.open_nodes.sort()
					lowerCAmelCase  :     List[Any]       						= self.open_nodes.pop(0						)

					if current_node.pos == self.target.pos:
							lowerCAmelCase  :     Tuple       						= True
							return self.retrace_path(a_						)

					self.closed_nodes.append(a_						)
					lowerCAmelCase  :     Tuple       						= self.get_successors(a_						)

					for child_node in successors:
							if child_node in self.closed_nodes:
									continue

							if child_node not in self.open_nodes:
									self.open_nodes.append(a_						)
							else:
									# retrieve the best current path
									lowerCAmelCase  :     int       						= self.open_nodes.pop(self.open_nodes.index(a_						)						)

									if child_node.g_cost < better_node.g_cost:
											self.open_nodes.append(a_						)
									else:
											self.open_nodes.append(a_						)

			if not self.reached:
					return [self.start.pos]
			return None



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= []
			for action in delta:
					lowerCAmelCase  :     Dict       						= parent.pos_x + action[1]
					lowerCAmelCase  :     str       						= parent.pos_y + action[0]

					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(a_						) - 1):
							continue

					if grid[pos_y][pos_x] != 0:
							continue

					successors.append(
					    Node(
					        a_		,   a_		,   self.target.pos_y		,   self.target.pos_x		,   parent.g_cost + 1		,   a_		,   )						)
			return successors





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= node
			lowerCAmelCase  :     List[str]       						= []
			while current_node is not None:
					path.append((current_node.pos_y, current_node.pos_x)						)
					lowerCAmelCase  :     Dict       						= current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
					lowerCAmelCase									=   (0, 0)
					lowerCAmelCase									=   (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("""------""")

					lowerCAmelCase									=   GreedyBestFirst(init, goal)
					lowerCAmelCase									=   greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															lowerCAmelCase									=   2

										for elem in grid:
															print(elem)



